branch updated

app.py

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+from datetime import date
+from datetime import datetime
+import re
+
+import numpy as np  
+import pandas as pd 
+from PIL import Image
+import plotly.express as px  
+import plotly.graph_objects as go
+import streamlit as st  
+import time
+
+from plotly.subplots import make_subplots
+
+# Read CSV file into pandas and extract timestamp data
+### YOUR LINE OF CODE HERE
+dfSentiment = pd.read_csv("../TSLASentimentAnalyzer/sentiment_data.csv")
+dfSentiment['timestamp'] = [datetime.strptime(dt, '%Y-%m-%d') for dt in dfSentiment['timestamp'].tolist()]
+
+# Multi-select columns to build chart
+col_list = dfSentiment.columns.values.tolist() ### YOUR LINE OF CODE HERE #### Extract columns into a list
+
+r_sentiment = re.compile(".*sentiment")
+sentiment_cols = list(filter(r_sentiment.match, col_list)) ### YOUR LINE OF CODE HERE
+
+r_post = re.compile(".*post")
+post_list = list(filter(r_post.match, col_list)) ### YOUR LINE OF CODE HERE
+
+r_perc= re.compile(".*perc")
+perc_list = list(filter(r_perc.match, col_list))
+
+r_close = re.compile(".*close")
+close_list = list(filter(r_close.match, col_list))
+
+r_volume = re.compile(".*volume")
+volume_list = list(filter(r_volume.match, col_list))
+
+sentiment_cols = sentiment_cols + post_list
+stocks_cols = perc_list + close_list + volume_list  ### YOUR LINE OF CODE HERE
+
+# Config for page
+st.set_page_config(
+    page_title= 'TSLA Subreddit and Stock Price', ### YOUR LINE OF CODE HERE
+    page_icon='✅',
+    layout='wide',
+)
+
+with st.sidebar:
+    # FourthBrain logo to sidebar
+    fourthbrain_logo = Image.open('./images/fourthbrain_logo.png')
+    st.image([fourthbrain_logo], width=300)
+
+    # Date selection filters
+    start_date_filter = st.date_input(
+        ### YOUR LINE OF CODE HERE
+        'Start Date',
+        min(dfSentiment['timestamp']),
+        min_value=min(dfSentiment['timestamp']),
+        max_value=max(dfSentiment['timestamp'])
+        )
+        
+
+    end_date_filter = st.date_input(
+        'End Date',
+        max(dfSentiment['timestamp']),
+        min_value=min(dfSentiment['timestamp']),
+        max_value=max(dfSentiment['timestamp'])
+        )
+
+    sentiment_select = st.selectbox('Select Sentiment/Reddit Data', ('sentiment_score', 'sentiment_score_lag1',
+    'perc_change_sentiment', 'sentiment_SMA3mo')) ### YOUR LINE OF CODE HERE
+    stock_select = st.selectbox('Select Stock Data', ('close', 'close_lag1', 'perc_change_close',
+    'volume')) ### YOUR LINE OF CODE HERE
+
+# Banner with TSLA and Reddit images
+tsla_logo = Image.open('./images/tsla_logo.png') ### YOUR LINE OF CODE HERE
+reddit_logo = Image.open('./images/reddit_logo.png')
+st.image([tsla_logo, reddit_logo], width=200)
+
+# dashboard title
+### YOUR LINE OF CODE HERE
+st.title('TSLA Subreddit and Stock Price')
+
+## dataframe filter
+# start date
+dfSentiment = dfSentiment[dfSentiment['timestamp'] >= datetime(start_date_filter.year, start_date_filter.month, start_date_filter.day)]
+    
+# end date
+dfSentiment = dfSentiment[dfSentiment['timestamp'] <= datetime(end_date_filter.year, end_date_filter.month, end_date_filter.day)] ### YOUR LINE OF CODE HERE
+dfSentiment = dfSentiment.reset_index(drop=True)
+
+
+# creating a single-element container
+placeholder = st.empty()  ### YOUR LINE OF CODE HERE
+
+# near real-time / live feed simulation
+for i in range(1, len(dfSentiment)-1):
+
+    # creating KPIs
+    last_close =  dfSentiment['close'][i]
+    last_close_lag1 = dfSentiment['close'][i-1]
+    last_sentiment = dfSentiment['sentiment_score'][i] ### YOUR LINE OF CODE HERE
+    last_sentiment_lag1 = dfSentiment['sentiment_score'][i-1] ### YOUR LINE OF CODE HERE
+
+
+    with placeholder.container():
+
+        # create columns
+        kpi1, kpi2, kip3 = st.columns(3)
+
+        # fill in those three columns with respective metrics or KPIs
+        kpi1.metric(
+            label='Sentiment Score',
+            value=round(last_sentiment, 3),
+            delta=round(last_sentiment_lag1, 3),
+        )
+        
+        kpi2.metric(
+            label='Last Closing Price',
+            ### YOUR LINE 1 OF CODE HERE
+            value=round(last_close, 3),
+            ### YOUR LINE 2 OF CODE HERE
+            delta=round(last_close_lag1, 3)
+        )
+        
+
+        # create two columns for charts
+        fig_col1, fig_col2 = st.columns(2)
+        
+        with fig_col1:
+            # Add traces
+            fig=make_subplots(specs=[[{"secondary_y":True}]])
+
+
+            fig.add_trace(                               
+                go.Scatter(                          
+                x=dfSentiment['timestamp'][0:i],
+                y=dfSentiment[sentiment_select][0:i],
+                name=sentiment_select,
+                mode='lines',                            
+                hoverinfo='none',                        
+                )              
+            )
+
+            if sentiment_select.startswith('perc') == True:
+                ### YOUR LINE OF CODE HERE
+                yaxis_label = 'Percentage'
+
+            elif sentiment_select in sentiment_cols:
+                ### YOUR LINE OF CODE HERE
+                yaxis_label = 'Sentiment'
+
+            elif sentiment_select in post_list:
+                yaxis_label = 'Volume'
+
+            fig.layout.yaxis.title=yaxis_label
+                                          
+            if stock_select.startswith('perc') == True:
+                fig.add_trace(                               
+                    go.Scatter(                          
+                    x=dfSentiment['timestamp'][0:i],
+                    y=dfSentiment[stock_select][0:i],
+                    name=stock_select,
+                    mode='lines',                            
+                    hoverinfo='none', 
+                    yaxis='y2',                    
+                    ) 
+                )
+                fig.layout.yaxis2.title='% Change Stock Price ($US)'
+            
+            elif stock_select == 'volume':
+                fig.add_trace(                               
+                    go.Scatter(                          
+                    x=dfSentiment['timestamp'][0:i],
+                    y=dfSentiment[stock_select][0:i],
+                    name=stock_select,
+                    mode='lines',                            
+                    hoverinfo='none', 
+                    yaxis='y2',                    
+                    ) 
+                )
+                
+                fig.layout.yaxis2.title="Shares Traded"
+
+
+            else:
+                fig.add_trace(                               
+                    go.Scatter(                          
+                    x=dfSentiment['timestamp'][0:i],
+                    y=dfSentiment[stock_select][0:i],
+                    name=stock_select,
+                    mode='lines',                            
+                    hoverinfo='none', 
+                    yaxis='y2',                    
+                    ) 
+                )
+
+                fig.layout.yaxis2.title='Stock Price ($USD)'
+
+
+            fig.layout.xaxis.title='Timestamp'
+
+            # write the figure throught streamlit
+            ### YOUR LINE OF CODE HERE
+            st.plotly_chart(fig)
+
+        st.markdown('### Detailed Data View')
+        st.dataframe(dfSentiment.iloc[:, 1:][0:i])
+        time.sleep(1)

requirements.txt

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+numpy==1.22.3
+pandas==1.4.3
+Pillow==9.2.0
+plotly==5.6.0
+streamlit==1.10.0

sentiment_data.csv

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+,timestamp,counter,close,volume,sentiment_score,close_lag1,perc_change_close,sentiment_score_lag1,perc_change_sentiment,sentiment_SMA3mo
+1,2022-03-01,298908,1099.56995,40225400,0.8713025534362123,932.0,0.17979608369098718,0.8656592077138473,0.006519130937529968,0.0
+2,2022-04-01,204020,1145.44995,45377900,0.8560624771779126,1099.56995,0.04172540364530686,0.8713025534362123,-0.01749114150784527,0.8643414127759907
+3,2022-05-01,394149,952.62,48324400,0.8597976830753967,1145.44995,-0.1683442825240858,0.8560624771779126,0.004363239830108604,0.8623875712298404
+4,2022-06-01,78141,775.0,40931000,0.8685562434743662,952.62,-0.18645419999580107,0.8597976830753967,0.010186769017149638,0.8614721345758918
+5,2022-07-01,190969,681.78998,24781500,0.8626362161723645,775.0,-0.12027099354838708,0.8685562434743662,-0.006815940068913239,0.8636633809073758